Poisson regression for modeling count and frequency outcomes in trauma research.
The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.
Gagnon
David R
DR
Boston University School of Public Health, and Massachusetts Veterans Epidemiology Research, and Information Center, VA Boston Healthcare System, Boston, MA 02130, USA. gagnon@bu.edu
Doron-LaMarca
Susan
S
Bell
Margret
M
O'Farrell
Timothy J
TJ
Taft
Casey T
CT
D000374:Aggression; D000437:Alcoholism / Q000523:psychology / Q000534:rehabilitation; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D016012:Poisson Distribution; D012106:Research; D012747:Sexual Partners / Q000523:psychology; D014947:Wounds and Injuries